Probabilistic Machine Learning: An Introduction
-15%
Availability: Out of Stock

Probabilistic Machine Learning: An Introduction

25
SKU: 9780262046824

Original price was: ₹10,650.00.Current price is: ₹9,052.50.

Dive into the world of machine learning with “Probabilistic Machine Learning: An Introduction” by Kevin P. Murphy. This comprehensive guide offers a detailed exploration of machine learning through the lens of probabilistic modeling and Bayesian decision theory. From linear regression to deep learning, uncover the mathematical foundations and practical applications. Explore cutting-edge topics like transfer learning and unsupervised learning. 9780262046824

Out of stock

Category: Tag:

Description

  • ISBN-13: 9780262046824
  • Publisher: Mit Press
  • Publisher Imprint: Mit Press
  • Height: 494 mm
  • No of Pages: 944
  • Series Title: Adaptive Computation and Machine Learning
  • Width: 237 mm
  • ISBN-10: 0262046822
  • Publisher Date: 01 Feb 2022
  • Binding: Hardback
  • Language: English
  • Returnable: Y
  • Spine Width: 43 mm
  • Weight: 1518 gr

25 reviews for Probabilistic Machine Learning: An Introduction

  1. Vaibhav Vivek Sahi –

    I found the Bayesian approach fascinating. However, some chapters were hard to follow.

  2. Ishika Arya –

    Math is not well explained. However, I was able to understand the concept after some time.

  3. Abhay Kumar Maurya –

    It is very verbose. However, the content is solid and insightful

  4. Utkarsh Bansal –

    One of the best books on the topic. Clear explanations and relevant examples.

  5. Geetanjali Mukherjee –

    Excellent resource for understanding the math behind ML. Highly recommended.

  6. Siddharth Bhandari –

    Some topics are not explained clearly. Assumes a good background in statistics.

  7. Himanshu –

    Concepts are so well explained, that it becomes the best choice for the person.

  8. Tarun Ohlyan –

    I wish the book was more detailed, but overall it serves the purpose

  9. Soumya Ranjan Katha –

    A solid intro, but requires existing math knowledge. Some parts felt rushed. Good examples though.

  10. Akanshit Narula –

    Dense and challenging. It’s a thorough resource, but not for absolute beginners. Needs patience.

  11. Vaani Kaushik –

    A decent introduction, but not the easiest to digest. Expect to reread sections.

  12. Devanshu Agrawal –

    A great introduction to the probabilistic approach to ML. Highly recommended!

  13. Harshit Agrawal –

    The best ML book I’ve read so far. Clear, concise, and well-organized.

  14. Priyank Agrawal –

    A bit overwhelming. Needs more real-world examples to balance the theory.

  15. Rajat Sharan Sethi –

    Okay book for understanding fundamentals, but hard to implement by self.

  16. Manisha Kumari Gobind Prajapati –

    Great book! Provides a solid foundation in probabilistic machine learning.

  17. Diksha Munjal –

    Comprehensive and well-written. It’s now my go-to reference for ML.

  18. Aayush Sharma –

    The book dives into the details, this is a complete package for ML. Loved it!

  19. Kapil Yadav –

    Too theoretical for my taste. Needed more practical exercises. Okayish book.

  20. Ashtha –

    Excellent book! Murphy explains complex concepts clearly. A must-read for anyone serious about ML.

  21. Priya –

    Good, but could use more visual aids. Some concepts are hard to visualize.

  22. Jyoti Baghel –

    Detailed and rigorous. A valuable addition to any machine learning library.

  23. Drishti Singh –

    Difficult to get through, but rewarding. A very thorough introduction.

  24. Mahi Sachdeva –

    Good explanations, but the book is quite long. It could be more concise.

  25. Manan Anand –

    Comprehensive coverage, but a bit dry. The code examples are helpful. Good resource overall.

Add a review

Your email address will not be published. Required fields are marked *

Related products